Combination of dynamic TOPMODEL and machine learning techniques to improve runoff prediction
Abstract TOPMODEL has been widely employed in hydrology research, undergoing continuous modifications to broaden its practical applicability and enhance its simulation accuracy. To encompass spatial discretization, diffusion‐wave characteristics, depth‐dependent flow velocity, and flux estimation in...
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Wiley
2025-03-01
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| Series: | Journal of Flood Risk Management |
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| Online Access: | https://doi.org/10.1111/jfr3.13050 |
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| author | Pin‐Chun Huang |
| author_facet | Pin‐Chun Huang |
| author_sort | Pin‐Chun Huang |
| collection | DOAJ |
| description | Abstract TOPMODEL has been widely employed in hydrology research, undergoing continuous modifications to broaden its practical applicability and enhance its simulation accuracy. To encompass spatial discretization, diffusion‐wave characteristics, depth‐dependent flow velocity, and flux estimation in the unsaturated zone, a generalized dynamic TOPMODEL is developed by introducing a greater number of physical parameters. The present study aims to evaluate the optimal combination of these parameters within the dynamic TOPMODEL framework using machine learning techniques to improve the accuracy of runoff predictions and bolster the model's reliability. An innovative training method is suggested to elevate the model's performance by integrating the Long Short‐Term Memory (LSTM) algorithm and a topological classification, which relies on the evolving spatial distribution of runoff conditions during floods. The research findings show that the proposed methodology achieves the lowest mean relative error (MRE) at 0.106, the highest Pearson correlation coefficient (PC) at 0.938, and the highest coefficient of determination (R2) at 0.906 among the three dynamic TOPMODEL types adopted in this study. The effective implementation of a case study in a river basin showcases the feasibility of the proposed method in conjunction with dynamic TOPMODEL and underscores the importance of employing the suggested training procedure. |
| format | Article |
| id | doaj-art-266a71b7f3204d3196d77db1d8b2b309 |
| institution | OA Journals |
| issn | 1753-318X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Flood Risk Management |
| spelling | doaj-art-266a71b7f3204d3196d77db1d8b2b3092025-08-20T02:10:27ZengWileyJournal of Flood Risk Management1753-318X2025-03-01181n/an/a10.1111/jfr3.13050Combination of dynamic TOPMODEL and machine learning techniques to improve runoff predictionPin‐Chun Huang0Department of Harbor and River Engineering National Taiwan Ocean University Keelung TaiwanAbstract TOPMODEL has been widely employed in hydrology research, undergoing continuous modifications to broaden its practical applicability and enhance its simulation accuracy. To encompass spatial discretization, diffusion‐wave characteristics, depth‐dependent flow velocity, and flux estimation in the unsaturated zone, a generalized dynamic TOPMODEL is developed by introducing a greater number of physical parameters. The present study aims to evaluate the optimal combination of these parameters within the dynamic TOPMODEL framework using machine learning techniques to improve the accuracy of runoff predictions and bolster the model's reliability. An innovative training method is suggested to elevate the model's performance by integrating the Long Short‐Term Memory (LSTM) algorithm and a topological classification, which relies on the evolving spatial distribution of runoff conditions during floods. The research findings show that the proposed methodology achieves the lowest mean relative error (MRE) at 0.106, the highest Pearson correlation coefficient (PC) at 0.938, and the highest coefficient of determination (R2) at 0.906 among the three dynamic TOPMODEL types adopted in this study. The effective implementation of a case study in a river basin showcases the feasibility of the proposed method in conjunction with dynamic TOPMODEL and underscores the importance of employing the suggested training procedure.https://doi.org/10.1111/jfr3.13050dynamic TOPMODELmachine learningrunoff prediction |
| spellingShingle | Pin‐Chun Huang Combination of dynamic TOPMODEL and machine learning techniques to improve runoff prediction Journal of Flood Risk Management dynamic TOPMODEL machine learning runoff prediction |
| title | Combination of dynamic TOPMODEL and machine learning techniques to improve runoff prediction |
| title_full | Combination of dynamic TOPMODEL and machine learning techniques to improve runoff prediction |
| title_fullStr | Combination of dynamic TOPMODEL and machine learning techniques to improve runoff prediction |
| title_full_unstemmed | Combination of dynamic TOPMODEL and machine learning techniques to improve runoff prediction |
| title_short | Combination of dynamic TOPMODEL and machine learning techniques to improve runoff prediction |
| title_sort | combination of dynamic topmodel and machine learning techniques to improve runoff prediction |
| topic | dynamic TOPMODEL machine learning runoff prediction |
| url | https://doi.org/10.1111/jfr3.13050 |
| work_keys_str_mv | AT pinchunhuang combinationofdynamictopmodelandmachinelearningtechniquestoimproverunoffprediction |